openHSU – Research Showcase

4527
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779
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140
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109
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31
Conferences
17
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  • Publication
    Open Access
    Nanofabrication of model electrodes for photoelectrochemical applications
    (UB HSU, 2024-06-25)
    Kollmann, Jiri
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    Helmut-Schmidt-Universität / Universität der Bundeswehr Hamburg
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    Scharp, Ian
    Anthropogenic climate change is one of the biggest problems facing society today, and counteracting it requires replacing the fossil fuel-based infrastructure with renewable energy alternatives, such as green hydrogen. Photoelectrochemical cells (PECs) offer a sustainable way to perform solar water splitting, and thus to convert energy provided by the sun into chemical form through the generation of hydrogen. A main component of PECs are photoelectrodes, which are manufactured with semiconducting materials in order to absorb light, with generation of charge carriers, which contribute to drive the photoelectrochemical reactions at the interface to the electrolyte. The structure of photoelectrodes at the nanoscale determines their performance for light absorption, carrier generation, and carrier transport and transfer. Nanofabrication methods are a group of technologies that enable the construction of interfaces and devices with nanometer precision. In this thesis, nanofabrication methods, in particular atomic layer deposition (ALD), are explored for the manufacture of individual components for photoelectrodes. The resulting photoelectrode components are investigated in terms of performance as well as stability, which are the main challenges towards large scale implementation of PEC technology. Stability has the most significant impact on the longevity and hence industrial applicability of photoelectrochemical cells. A significant part of this work focuses on the synthesis and investigation of thin coatings to protect photoelectrodes from corrosion. Thin films of TiO2 were deposited by atomic layer deposition, and the influence of deposition conditions on crystallinity, and on optoelectronic and photoelectrochemical properties were investigated. The effect of film structure on stability under different photoelectrochemical operating conditions was directly quantified by ex-situ spectroscopic ellipsometry. An additional significant component of modern photoelectrodes are layers for selective charge transport, usually transparent conducting oxides, which help extract the generated charge carriers from the photoabsorber. In this work, a series of transparent conductive oxides (ZnO:Al, ZnO:Hf, ZnO:Ti) with different doping atoms at various doping concentrations were obtained by atomic layer deposition using supercycles, and their optoelectronic properties were characterized, compared, and optimized with respect to resistivity. A further step towards large-scale deployment of PECs requires the implementation of materials capable of optimally using the solar spectrum. In this work, selected earth-abundant photoabsorbers (Fe2O3, CuO, Cu2O) with suitable band gaps for solar water splitting, were synthesized by conformal and uniform atomic layer deposition, and approaches were developed towards the possible first time synthesis of the promising photoabsorber CuFeO2 by the atomic layer deposition. Finally, using the nanofabrication methods of direct write lithography and reactive ion etching, different reproducible geometric structures with varying aspect ratios and high specific surface areas were created in square and hexagonal arrangements in silicon wafers, in order to improve the studied absorption properties and photocurrent densities of deposited photoabsorbers by orthogonalization of the light absorption and charge separation processes.
  • Publication
    Metadata only
    Accuracy and performance evaluation of low density internal and external flow predictions using CFD and DSMC
    (Elsevier, 2024-06-18) ; ;
    Samanta, Amit K.
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    Küpper, Jochen
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    Amin, Muhamed
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    The Direct Simulation Monte Carlo (DSMC) method was widely used to simulate low density gas flows with large Knudsen numbers. However, DSMC encounters limitations in the regime of lower Knudsen numbers (Kn<0.05). In such cases, approaches from classical computational fluid dynamics (CFD) relying on the continuum assumption are preferred, offering accurate solutions at acceptable computational costs. In experiments aimed at imaging aerosolized nanoparticles in vacuo a wide range of Knudsen numbers occur, which motivated the present study on the analysis of the advantages and drawbacks of DSMC and CFD simulations of rarefied flows in terms of accuracy and computational effort. Furthermore, the potential of hybrid methods is evaluated. For this purpose, DSMC and CFD simulations of the flow inside a convergent–divergent nozzle (internal expanding flow) and the flow around a conical body (external shock generating flow) were carried out. CFD simulations utilize the software OpenFOAM and the DSMC solution is obtained using the software SPARTA. The results of these simulation techniques are evaluated by comparing them with experimental data (1), evaluating the time-to-solution (2) and the energy consumption (3), and assessing the feasibility of hybrid CFD-DSMC approaches (4).
  • Publication
    Metadata only
    Customizable Memory Training in Virtual Reality with Personal Memoirs
    (IEEE, 2021-12-22)
    Schmucker, Vanessa
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    Eiler, Tanja Joan
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    Grunewald, Armin
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    Forstmeier, Simon
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    Gieber, Christian
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    Bruck, Rainer
    Memory training methods are often not designed for all age groups, which causes older people to be failed by educational systems. However, the brain remains plastic for a lifetime. The application discussed in this paper demonstrates how approaches to learning psychology can be implemented in virtual reality to create an individualized learning environment that addresses all age groups. In addition, personal memoirs were used to address the episodic part of the brain and to increase motivation. In a small usability study, twelve participants tested the application and filled out a questionnaire. This showed that the design and usability of this application, using a virtual learning environment customized to the user as well as different perception channels, has been successful. Moreover, the wellbeing was rated positively as well.
  • Publication
    Metadata only
    Querying Large Knowledge Graphs over Triple Pattern Fragments: An Empirical Study
    (Springer Nature Switzerland, 2018-09-18)
    Heling, Lars
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    Acosta, Maribel
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    Sure-Vetter, York
    Triple Pattern Fragments (TPFs) are a novel interface for accessing data in knowledge graphs on the web. So far, work on performance evaluation and optimization has focused mainly on SPARQL query execution over TPF servers. However, in order to devise querying techniques that efficiently access large knowledge graphs via TPFs, we need to identify and understand the variables that influence the performance of TPF servers on a fine-grained level. In this work, we assess the performance of TPFs by measuring the response time for different requests and analyze how the requests’ properties, as well as the TPF server configuration, may impact the performance. For this purpose, we developed the Triple Pattern Fragment Profiler to determine the performance of TPF server. The resource is openly available at https://doi.org/10.5281/zenodo.1211621 Titel anhand dieser DOI in Citavi-Projekt übernehmen. To this end, we conduct an empirical study over four large knowledge graphs in different server environments and configurations. As part of our analysis, we provide an extensive evaluation of the results and focus on the impact of the variables: triple pattern type, answer cardinality, page size, backend and the environment type on the response time. The results suggest that all variables impact on the measured response time and allow for deriving suggestions for TPF server configurations and query optimization. © Springer Nature Switzerland AG 2018.
  • Publication
    Metadata only
    Adaptable Interfaces, Interactions, and Processing for Linked Data Platform Components
    (Association for Computing Machinery, 2017-09-11)
    Keppmann, Felix Leif
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    Harth, Andreas
    Currently, we are witnessing the rise of new technology-driven trends such as the Internet of Things, Web of Things, and Factories of the Future that are accompanied by an increasingly heterogeneous landscape of highly modularized devices and pervasion of network-Accessible "things" within all areas of life. At the same time, we can observe increasing complexity of the task of integrating subsets of heterogeneous components into applications that fulfil certain needs by providing value-Added functionality beyond the pure sum of their components. Enabling integration in these multi-stakeholder scenarios requires new architectural approaches for adapting components, while building on existing technologies and thus ensuring broader acceptance. To this end, we present our approach on adaptation, that introduces adaptable interfaces, interactions, and processing for Linked Data Platform components. In addition, we provide an implementation of our approach that enables the adaptation of components via a thin meta-layer defined on top of the components' domain data and functionality. Finally, we evaluate our implementation by using a distributed benchmark environment and adapting interfaces, interactions, and processing of the involved components at runtime. © 2017 Copyright held by the owner/author(s).
  • Publication
    Metadata only
    A Model-driven Approach for the Description of Blockchain Business Networks
    (University of Hawaiʻi at Māno, Hamilton Library, 2018-01-03)
    Seebacher, Stefan
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    The concept of blockchain technology has gained significant momentum in practice and research in the past few years, as it provides an effective way for addressing the issues of anonymity and traceability in distributed scenarios with multiple parties, which have to exchange information and want to securely collaborate with each other. However, up-to-date, the impact of the structure and setup of business networks on successfully applying blockchain technology, remains largely unexplored. We propose a model-driven approach, combining an ontology and a layer model, that is capable of capturing the properties of existing blockchain-driven business networks. The layers are used to facilitate the comprehensive description of such networks. We also introduce the Blockchain Business Network Ontology (BBO), formalizing the concepts and properties for describing the integral parts of a blockchain network. We show the practical applicability of our work by evaluating and applying it to an available blockchain use case. © 2018 IEEE Computer Society. All rights reserved.
  • Publication
    Metadata only
    Adaptive semantic process modeling tool
    (RWTH, 2016-09-30)
    Weller, Tobias
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    Processes need to be captured in a structured way in order to analyse them by using computer-assisted methods. This circumstance becomes more important as processes become complex. Business Process Model and Notation appears as de factor standard in industry as process modeling language. However, it has a limit that semantics like e.g input/output parameters, involved persons or references to external data sources are not captured. This circumstance leads to a negligence of important semantic information. Semantic information can be used in process analysis to enhance them and find new insights. In addition are process modeling languages often extended with new elements to adapt its expressiveness to latest scenarios, as well as to model scenarios in specific domains. To address these problems we 1) allow users to define BPMN elements, as well as corresponding semantics for them; 2) provide an open-source tool to capture BPMN process models graphically in a Semantic MediaWiki; 3) publish the information according to the Linked Data principles and 4) show that the system is easy extensible to latest process elements.
  • Publication
    Metadata only
    Cognitive process designer - An open-source tool to capture processes according to the linked data principles
    (RWTH, 2016-07-09)
    Weller, Tobias
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    Processes need to be captured in a structured way in order to analyze them by using computer-assisted methods. This circumstance becomes more important as the process becomes complex. Although there are standardized formats, they do not capture semantics of input/output parameters, involved persons or references to external data sources. Existing solutions provide tools to capture processes locally and specify new properties to extend the semantics of process languages. However, a collaborative platform to capture, discuss and share information is more advantageous, because processes are usually used and maintained collaboratively. In addition, users cannot define own semantics for their use-case scenarios and the proposed semantics and processes are not published according to the Linked Data principles. To address these problems we 1) provide an open-source tool to capture BPMN processes graphically in a Semantic MediaWiki; 2) allow users to define own semantics and 3) publish the information according to the Linked Data principles.
  • Publication
    Metadata only
    A semantic framework for sequential decision making for journal of web engineering
    (Rinton Press, 2017-03-01)
    Philipp, Patrick
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    Rettinger, Achim
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    Katic, Darko
    Current developments in the medical domain, not unlike many other sectors, are marked by the growing digitalization of data, including patient records, study results, clinical guidelines or imagery. This trend creates the opportunity for the development of innovative decision support systems to assist physicians in making a diagnosis or preparing a treatment plan. Similar conditions hold for the Web, where massive amounts of raw text are to be processed and interpreted automatically, e.g. to eventually add new information to a knowledge base. To this end, complex tasks need to be solved, requiring one or more interpretation algorithms (e.g. image- or natural language processors) to be chosen and executed based on heterogeneous data. We, therefore, propose the first approach to a semantic framework for sequential decision making and develop the foundations of a Linked agent who executes interpretation algorithms available as Linked APIs [43] on a data-driven, declarative basis [45] by integrating structured knowledge formalized with the Resource Description Framework (RDF), and having access to meta components for planning and learning from experience. We evaluate our framework based on automatically processing brain images, the ad-hoc combination of surgical phase recognition algorithms and experiential learning to optimally pipeline entity linking approaches. © Rinton Press.
  • Publication
    Metadata only
    Multi-purpose Adaptation in the Web of Things
    (Springer International Publishing, 2017-05-04)
    Terdjimi, Mehdi
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    Médini, Lionel
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    Mrissa, Michael
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    Web of Things applications require advanced solutions to provide adaptation to different purposes from common context models. While such models are application-specific, the adaptation itself is based on questions (i.e. concerns) that are orthogonal to application domains. In this paper, we present a generic solution to provide reusable and multi-purpose context-based adaptation for smart environments. We rely on semantic technologies and reason about contextual information to evaluate, at runtime, the pertinence of each adaptation possibility to adaptation questions covering various concerns. We evaluate our solution against a smart agriculture scenario using the ASAWoO platform, and discuss how to design context models and rules from “classical” information sources (e.g. domain experts, device QoS, user preferences). © Springer International Publishing AG 2017.